{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.117490Z",
     "end_time": "2024-06-19T10:57:34.134422Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "card = pd.read_csv(r\"../data/bank/card.csv\", encoding=\"gbk\")\n",
    "disp = pd.read_csv(r\"../data/bank/disp.csv\", encoding=\"gbk\")\n",
    "clients = pd.read_csv(r\"../data/bank/clients.csv\", encoding=\"gbk\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.137422Z",
     "end_time": "2024-06-19T10:57:34.156896Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   card_id  disp_id      issued type_x  client_id  account_id type_y sex  \\\n0     1005     9285  1993-11-07    普通卡       9593        7753    所有者   女   \n1      104      588  1994-01-19    普通卡        588         489    所有者   女   \n2      747     4915  1994-02-05    普通卡       4915        4078    所有者   男   \n3       70      439  1994-02-08    普通卡        439         361    所有者   女   \n4      577     3687  1994-02-15    普通卡       3687        3050    所有者   男   \n\n   birth_date  district_id  \n0  1968-01-28           74  \n1  1960-10-20           61  \n2  1963-07-19           40  \n3  1968-09-12           51  \n4  1972-02-06           49  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>card_id</th>\n      <th>disp_id</th>\n      <th>issued</th>\n      <th>type_x</th>\n      <th>client_id</th>\n      <th>account_id</th>\n      <th>type_y</th>\n      <th>sex</th>\n      <th>birth_date</th>\n      <th>district_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>女</td>\n      <td>1968-01-28</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>104</td>\n      <td>588</td>\n      <td>1994-01-19</td>\n      <td>普通卡</td>\n      <td>588</td>\n      <td>489</td>\n      <td>所有者</td>\n      <td>女</td>\n      <td>1960-10-20</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>747</td>\n      <td>4915</td>\n      <td>1994-02-05</td>\n      <td>普通卡</td>\n      <td>4915</td>\n      <td>4078</td>\n      <td>所有者</td>\n      <td>男</td>\n      <td>1963-07-19</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>70</td>\n      <td>439</td>\n      <td>1994-02-08</td>\n      <td>普通卡</td>\n      <td>439</td>\n      <td>361</td>\n      <td>所有者</td>\n      <td>女</td>\n      <td>1968-09-12</td>\n      <td>51</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>577</td>\n      <td>3687</td>\n      <td>1994-02-15</td>\n      <td>普通卡</td>\n      <td>3687</td>\n      <td>3050</td>\n      <td>所有者</td>\n      <td>男</td>\n      <td>1972-02-06</td>\n      <td>49</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(card, disp, on=\"disp_id\", how=\"left\")\n",
    "data = pd.merge(data, clients, on=\"client_id\", how=\"left\")\n",
    "data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.156896Z",
     "end_time": "2024-06-19T10:57:34.191278Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   card_id  disp_id      issued type sex  birth_date  district_id\n0     1005     9285  1993-11-07  普通卡   女  1968-01-28           74\n1      104      588  1994-01-19  普通卡   女  1960-10-20           61\n2      747     4915  1994-02-05  普通卡   男  1963-07-19           40\n3       70      439  1994-02-08  普通卡   女  1968-09-12           51\n4      577     3687  1994-02-15  普通卡   男  1972-02-06           49",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>card_id</th>\n      <th>disp_id</th>\n      <th>issued</th>\n      <th>type</th>\n      <th>sex</th>\n      <th>birth_date</th>\n      <th>district_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>女</td>\n      <td>1968-01-28</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>104</td>\n      <td>588</td>\n      <td>1994-01-19</td>\n      <td>普通卡</td>\n      <td>女</td>\n      <td>1960-10-20</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>747</td>\n      <td>4915</td>\n      <td>1994-02-05</td>\n      <td>普通卡</td>\n      <td>男</td>\n      <td>1963-07-19</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>70</td>\n      <td>439</td>\n      <td>1994-02-08</td>\n      <td>普通卡</td>\n      <td>女</td>\n      <td>1968-09-12</td>\n      <td>51</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>577</td>\n      <td>3687</td>\n      <td>1994-02-15</td>\n      <td>普通卡</td>\n      <td>男</td>\n      <td>1972-02-06</td>\n      <td>49</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['client_id', 'account_id', 'type_y'], axis=1)\n",
    "data.columns = ['card_id', 'disp_id', 'issued', 'type', 'sex', 'birth_date', 'district_id']\n",
    "data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.180739Z",
     "end_time": "2024-06-19T10:57:34.195277Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='issued_year'>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#总体发卡趋势\n",
    "from datetime import *\n",
    "\n",
    "data['issued_date'] = pd.to_datetime(data['issued'])\n",
    "data['issued_year'] = data['issued_date'].map(lambda x: x.year)\n",
    "data['card_id'].groupby(data['issued_year']).count().plot(kind=\"bar\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.195277Z",
     "end_time": "2024-06-19T10:57:34.444868Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: ylabel='count'>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2不同卡的分布\n",
    "data['type'].value_counts().plot(kind=\"pie\", autopct='%.1f%%')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.383880Z",
     "end_time": "2024-06-19T10:57:34.595000Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='issued_year'>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分类型发卡趋势\n",
    "pd.crosstab(data['issued_year'], data['type']).plot(kind='bar')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.488998Z",
     "end_time": "2024-06-19T10:57:34.773155Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='issued_year'>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分类型发卡占比\n",
    "t1 = pd.crosstab(data['issued_year'], data['type'])\n",
    "t1[\"sum1\"] = t1.sum(1)\n",
    "t2 = t1.div(t1.sum1, axis=0)\n",
    "t2.drop('sum1', axis=1).plot(kind='bar', stacked=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.770158Z",
     "end_time": "2024-06-19T10:57:34.987782Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#面积图\n",
    "labels = [\"青年卡\", \"普通卡\", \"金卡\"]\n",
    "y1 = t1.loc[:, \"青年卡\"].astype('int')\n",
    "y2 = t1.loc[:, \"普通卡\"].astype('int')\n",
    "y3 = t1.loc[:, \"金卡\"].astype('int')\n",
    "x = t1.index.astype('int')\n",
    "plt.stackplot(x, y1, y2, y3, labels=labels)\n",
    "plt.title('发卡趋势')\n",
    "plt.ylabel('发卡量')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:34.988781Z",
     "end_time": "2024-06-19T10:57:35.169075Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='type'>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3、不同持卡人的性别对比\n",
    "sub_sch = pd.crosstab(data['type'], data['sex'])\n",
    "sub_sch.div(sub_sch.sum(1), axis=0).plot(kind='bar', stacked=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:35.172088Z",
     "end_time": "2024-06-19T10:57:35.341358Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from stack2dim import *\n",
    "\n",
    "stack2dim(data, 'type', 'sex')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:35.343356Z",
     "end_time": "2024-06-19T10:57:35.498200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='type', ylabel='age1'>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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xmjlzpqxWq5YuXert8gAAgI8yZEzL2LFjNXbs2PPa+/btqw0bNmjv3r1KSkpSVFSUAdUBAABfZPhA3F+KiYlRamqq0WUAAAAfU+vQcscdd6iiouKi823cuPGSCgIAAKhJrUPLtGnTNGrUKD3yyCO68cYbL2dNAAAA56l1aOnSpYuysrI0evRopaWlqXnz5pezLgAAgGrqNKblhhtu0Icffih/f58bCgMAABq5OqePcw80BAAA8KZ63RG3oqJC//M//6OHHnpIvXr10v79+/Xggw+qoKCgoesDAACQVM/QMmXKFK1fv15paWlyOBwKCgpS586dNWnSpIauDwAAQFI9Q8vHH3+suXPn6re//a38/Pzk5+enoUOH6ptvvmno+gAAACTVM7S0bNlSO3bs8Ly2WCw6cOCA4uLiGqwwAACAn6vXZUDPPfecRo4cqZUrV+rMmTOaPn26vvrqK2VmZjZ0fQAAAJLqGVpuueUWffjhh/roo4903XXXqWXLlnruuecUHx/f0PUBAABIqmdoWbZsmfr376/hw4efN83hcCgkJOSSCwMAAPi5eo1peeedd5Sfn6+PPvpI3333nad98uTJ6tGjh955550GKxAAAECqZ2i55ZZbNGTIEC1ZskSDBw/2jGX54IMPlJWVpaVLlzZokQAAAPU6PfT+++9r+fLlSkhIUHFxsXr16qXBgwersrJSCQkJOnz4cEPXCQAAmrh6hZbmzZtr8+bNCgoK0tdff62goCDZ7XaFhoaqvLxcQUFBDV0nAABo4up1eujll1/Wpk2bdN999+nNN99Ur1699Pjjj+uqq67Siy++qBtuuKGh6wQAAE1cvXpaEhIS9N5771VrO3jwoCIiIvTXv/5VXbt2bZDiAAAAzqlXaMnPz9fMmTN16NAhVVVVSZLcbreOHTumPXv2NGiBAAAAUj1PD6Wnp6tNmza66aablJiYqEmTJikwMFDjxo1r6PoAAAAk1TO05OXl6YknntDAgQN15MgR9e7dWxkZGcrJyWno+gAAACTV8/RQ69attWrVKj3++OMqKCjQyZMnFR0dre+//76h6wMA+CDrmWKjS4CP8Oa2UK/QMmHCBD3zzDN66KGHlJaWpjvuuEMWi0V9+vRp6PoAAD4o+NAWo0tAE1Sv0HLjjTfqs88+k3T2ic+9e/eWw+FQr169GrQ4AIBvOtP6FrmCI40uAz7AeqbYayG2XqFFkiwWi+e/b7rppgYpBgBgDq7gSLlCmhldBpqYeg3EBQAA8LZ697Q0Zgwwg8R2AAC+htBSAwaYAQDgewgtNWCAGSTvDi4DAFwcoaUGDDADAMD3MBAXAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYguGhZciQIcrJyZEkbd++XXfeeae6du2qxYsXG1wZAADwJYaGlr/85S/6/PPPJUlFRUUaMWKE7r77bq1YsUJr167Vtm3bjCwPAAD4EMNu419cXKzMzEy1bt1a0tkA07x5c40cOVIWi0VPPfWUVq9erW7dutVpuRZL/Wu6lPei8bJY2DaAc/gu4ELqu6+sy3sMCy2ZmZm67bbbVF5eLkn67rvv1LVrV1n+VX3Hjh31yiuv1Hm50dFh9a6pqCik3u9F4xUZGaJmzeq/XQGNCftJXIg39pWGhJZt27bpiy++0Lp165SRkSFJstvtatu2rWee0NBQHTt2rM7LPnGiVG53/eoqLnbU741o1IqLHfrpp1KjywB8AvtJXEh995UWS+07HLweWsrLyzV58mT94Q9/UGhoqKfdz89PNpvN8zowMFBlZWV1Xr7brXqHlvq+D43bpWxTQGPDdwEX4o19pdcH4s6fP1833HCDUlNTq7VHRESoqKjI89rhcCggIMDL1QEAAF/l9Z6WtWvX6uTJk0pOTpYklZWVaf369ZKkzp07e+bbt2+fWrRo4e3yAACAj/J6aFm+fLkqKys9r2fOnKmkpCQ98MADSk1N1datW5WSkqLXX39dPXv29HZ5AADAR3k9tLRs2bLa6yuuuEJRUVG68sorlZ6ermHDhumKK65QWFiYZsyY4e3yAACAjzLskudzfh5MBg0apJ49e+rgwYNKTk5WSAiX1gEAgLMMDy2/FB8fr/j4eKPLAAAAPsbwZw8BAADUBqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYAqEFAACYgr/RBQAAzMdadsroEuAjvLktEFoAALUWFhauAJtNOrjZ6FLgQwJsNoWFhV/2zyG0AABqLSamuf48e4FKS0uMLsVQhw8Xas6cWRo9eoxiY+ONLsdwYWHhiolpftk/h9BSA7o9IbEdABcSE9PcKwcoM4iNjVebNu2MLqPJILT8DN2e+CVvdXkCAC6O0PIzdHueRbfnv3mryxMAcHGGhZaSkhIdOnRIV199tSIiIowq4zx0e/4b3Z4AAF9iyH1a1q9frz59+mjChAnq3bu31q9fL0nKy8tTWlqaUlJSlJmZKbfbbUR5AADAB3k9tJSWlmrKlCl65513tHbtWk2aNEkvvfSSnE6nhg8frsTERGVnZys/P185OTneLg8AAPgor58estvtGj9+vNq3by9Juv7663Xy5Elt2bJFdrtd6enpCg4O1pgxYzRlyhSlpaXVafkWy+Woumk5tw4tFtYnANSE/WTDqcv683poueqqq9S/f39JUkVFhd5++23169dPubm5SkpKUnBwsCQpISFB+fn5dV5+dHRYg9bbFBUVhUiSIiND1KwZ6xMAfon9pDEMG4ibm5urwYMHKyAgQB999JHmz5+vuLg4z3SLxSKr1apTp07VaaDuiROlYijMpSkudnh+/vRTqcHVAIDvYT/ZcCyW2nc4GBZaEhIS9MYbb2j69OmaMGGC4uPjZbPZqs0TGBiosrKyOoUWt1uElkt0bv2xLgGgZuwnjWHYU54tFotuuOEGzZgxQ5988okiIiJUVFRUbR6Hw6GAgACDKgQAAL7E66Fl+/btyszM9Ly22WyyWCxq27atdu3a5WkvLCyU0+n0qXu4AAAA43g9tFx99dVauXKlVqxYoR9//FGzZs1Sjx491Lt3b9ntdmVnZ0uSsrKy1L17d/n5+Xm7RAAA4IO8HlqaN2+uOXPmaMmSJbr77rt15swZzZw5U/7+/srIyNDUqVPVtWtXbdy4UePGjfN2eQAAwEcZMhC3R48e+vDDD89r79u3rzZs2KC9e/cqKSlJUVFRBlQHAAB8kc89MDEmJkapqalGlwEAAHyMYVcPAQAA1AWhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmIK/0QUA8H1Hjx6Rw2E3ugzDhYSEqkWLlkaXATRZhBYAv6qk5JRGjXpSbrfL6FIMZ7VatWjREoWHRxhdSpNndJA+fLiw2k8jNaUwbUho+fTTTzV9+nT9+OOPuuaaazRr1iy1bdtWeXl5Sk9PV0FBgR588EE9//zzslgsRpQI4F/CwyM0d26W4QeIOXNmafToMYqNjTesjpCQUAKLD/ClID1nziyjS2hSYdrroaWgoEDjx4/XH/7wB910002aOnWqXnzxRS1ZskTDhw9Xz5499eqrryojI0M5OTlKS0vzdomG4y+Is5rSXw++zlf+P8TGxqtNm3ZGlwGD+UKQ9iVNKUx7PbTk5+dr7NixuuuuuyRJgwYN0pNPPqktW7bIbrcrPT1dwcHBGjNmjKZMmdLkQgt/QfxbU/rrAUDd+EqQhnd5PbTceuut1V4fOnRIv/nNb5Sbm6ukpCQFBwdLkhISEpSfn1/n5Zv9bFJERITmzeMvCOnsXw8REQQW/Pt7bbGY/zsOoLq6fKcNHYjrdDq1ePFiPfbYYyooKFBcXJxnmsVikdVq1alTp+p04IqODrscpXpVs2bm/x2AhlRUFCJJiowM4fsBNGGGhpa5c+cqODhYDz30kGbPni2bzVZtemBgoMrKyuoUWk6cKJXb3dCVAsY5fvyYSktLjC7DUN9/f3Z81Z49uSoudhhcjbHCwsIVE9Pc6DKABmOx1L7DwbDQ8sUXX2jZsmVauXKlAgICFBERof3791ebx+FwKCAgoE7LdbtFaEGjcfz4MT3z7AhVOJ1Gl+ITjB5n5QsCbDb9efYCgguaJENCS2FhocaOHatJkyapXbuzVwJ06NBBq1atqjaP0+lkTAOatNLSElU4nTrTprdcQXwXmjpr2Snp4GaVlpYQWtAkeT20lJWVafjw4erbt6/69esnh+NsV29ycrLsdruys7OVlpamrKwsde/eXX5+ft4uEfA5rqAIuUKaGV0GABjK66Hl888/14EDB3TgwAGtXLnS075x40ZlZGRo7NixmjlzpqxWq5YuXert8gAAgI/yemi57bbb9N1339U4LS4uThs2bNDevXuVlJSkqKgoL1cH+CbrmWKjS4APYDtAU+dzzx6KiYlRamqq0WUAPiX40BajSwAAw/lcaAFwvjOtb5ErONLoMmAw65liAiyaNEILYAKu4EgG4gJo8qxGFwAAAFAb9LQAJmAtO2V0CfABbAdo6ggtgA8LCwtXgM0mHdxsdCnwEQE2m8LCwo0uAzCExe1uXDe9/+knnj2ExoVnD0mHDxdqzpxZGj16jGJj440ux1A8ewiNjcVS+wcF09MC+LiYmOYcpP4lNjZebdq0M7oMAAZhIC4AADAFQgsAADAFQgsAADAFQgsAADAFQgsAADAFQgsAADAFQgsAADAFQgsAADAFQgsAADAFQgsAn1ZVVaX8/AOSpPz8A6qqqjK4IgBG4dlDAHzWl19u1dtvv6Hjx4952mJimmvw4CHq2rW7gZUBaCh1efYQPS0AfNKXX27VK6/MUKtWV+upp56RJD311DNq1epqvfLKDH355VaDKwTgbTwwEcBFHT16RA6H3Wuf53K59MYbWWrf/noNGPCQvvpquyTpyJEfNGDAQzp92qE338xSdHQzWa3e+9srJCRULVq09NrnAaiO00MAflVJySk98cTv5Ha7jC7FcFarVYsWLVF4eITRpQCNRl1ODxFaAFyUt3tadu3aqXffXSpJuu6663XttdcpICBAFRUVysv7Vt9+u0+SNGjQo+rUqYvX6qKnBWh4dQktnB4CcFHePlDb7WcDUmxsnI4fP+4JKZLUrFmMYmNjdfjwYbVrd63atGnn1doAGIfQAsDnWCwWSdLhw9+rS5dk3Xdfmmw2m5xOp77++u/aufPv1eYD0DQQWgD4nJMnizz/vWfPbk9IkSSbzVbjfAAaPy55BuBzSkpKfmWqpZbzAWhs6GkB4HNCQ0MlSeHhEVqw4A3t35+nkyeLFBV1pa655lqNGDFEJSWnPPMBaBroaQHgc84NxC0pOaVZs2bK3z9AN96YIn//AM2aNVMlJaeqzQegaaCnBYDPCQ8PlyS1bt1GBQX/0IQJz3mmNW/eQq1bt9GhQwc98wFoGggtAHxOdHQzSdI//nFInTsn6957Bygw0Kbycqd27fpKX3/992rzAWgaCC0AfE779tcrJqa5wsLCVVj4T+3cucMz7WxPS1vZ7aVq3/56A6sE4G2EFgA+x8/PT4MHD9Err8y4YE/L2LEvyM/Pz+hSAXgRt/EH4LO+/HKr3n77DR0/fszT1rx5C/3ud4+ra9fuBlYGoKHw7KFG9RsBTVtVVZVyc/d5Lnlu3/56eliARoTQ0qh+IwAAGq+6hBbu0wIAAEyB0AIAAEzBsNBSVFSkPn366Pvvv/e05eXlKS0tTSkpKcrMzFQjO3MFAAAugSGhpaioSMOHD9fhw4c9bU6nU8OHD1diYqKys7OVn5+vnJwcI8oDAAA+yJDQMmbMGN1zzz3V2rZs2SK73a709HS1atVKY8aM0erVq40oDwAA+CBDbi43depUxcfH609/+pOnLTc3V0lJSQoODpYkJSQkKD8/v87LtlguPg8AAPANdTluGxJa4uPjz2uz2+2Ki4vzvLZYLLJarTp16pQiIiJqvezo6NpdNgUAAMzFZ27j7+fnJ5vNVq0tMDBQZWVldQotJ05wnxYAAMzCYql9h4PPhJaIiAjt37+/WpvD4VBAQECdluN2i9ACAEAj5DOhpUOHDlq1apXndWFhoZxOZ516WSTGtAAAYCY+P6alJikpKbLb7crOzlZaWpqysrLUvXv3Oj9jhDEtAAA0ToY+eyghIUEbN270DMDduHGjxo4dq8DAQFmtVi1dulTt2rUzqjwAAOBDfO6BicePH9fevXuVlJSkqKgoo8sBAAA+wudCCwAAQE14YCIAADAFQgsAADAFQgsAADAFQgsAADAFQgsAn3T8+HGlpaVp9+7dRpcCwEcQWhqhyspKZWZmym63e9r++c9/KjMzU2VlZRd8n9PplNPp9EaJaCKKi4v13XffKT8/3/Nv9OjRmjhxYrW23NzcaturJK1Zs0ZOp1PXXXddvT573bp1SkxMFBdI4lJMnz5dX331ldFl4F985o64aDj+/v46ffq0FixYoOeee06StHz5cp04cUJBQUHV5n399dd1xRVX6D//8z+1Zs0azZ8/v9pdiCsrK3X06FHdcccdmjVr1gXvULxu3Tr9/ve/1549e2ThWQr4lyNHjmjTpk0KDAz0tB0/flx2u11/+9vfJElut1vl5eW65557FBoaKunsc8cWL16sgIAAPfDAA5Kk06dPKzg4WBaLRRUVFTpz5oxeeOEF3XXXXTV+ts1m0xVXXFFte3S5XKqsrDzv4axATdatW6fVq1fr4MGDmj9/fq2ehff111/rt7/9rTZv3qyWLVt6ocqmhdDSiCxYsECrVq2SzWZTZWWlXC6X1q9fL0k6evSoIiMj1adPH1VWVmrcuHHq37+/EhMTNWLECKWkpGjgwIEaOHCgcnNztXz5cj3//PN64YUXZLFY9Morr/zqIxU4QKAm7du317x58/TNN994dvjFxcWyWq36xz/+IelsD9+tt96q3/zmN573zZ07V507d9b8+fM9bcnJyVq8eHG1+X6N1WpVSUmJrr/+ek9bVVWVBg4cqD/+8Y8N8NuhMdu9e7f+9Kc/6a233tK6des0atQovfrqqwoODv7V953b34WEhHja2Bc2HEJLIzJixAj17t1bDodDKSkp6tOnj9auXas9e/Zo2bJl+vOf/6x33nlH1157rbp06SJJuvnmm/XEE09UW87VV1+tH374QbfeeqtSU1M1Y8aMiz4DigMEfsnlcunw4cOaOHGirNZ/n4meNWuWIiMj9fjjj1ebt6CgQPHx8dq3b5+WLVumNWvWXHINkZGR+vLLL6u1lZeXX/Jy0bj9/e9/17PPPquXX35ZHTp0UIcOHZSRkaGHH35Y06ZNU4cOHS743nPbeteuXT1tLpdLPXr00BtvvHHZa2/sCC2NzMmTJzVx4kR9+OGH500rKytTVlaWsrKyqnVzPv3005KkefPm6d1331VUVJSn+/3bb7/Vfffdp++//15LlixRx44dL/jZHCDwc3a7XbfddtsFp7/55pvntX322WcaPXq0HnzwwQs+d8ztdquqqkoul8vzl2tFRYWsVmutHrAaGBioioqKWnX1o2mpqqrS4sWLtXDhQs2bN0833XSTZ9qECRP01ltvadCgQUpNTdWgQYPUrVu3C25zO3bsqNbb8mvjCVF7hJZGpkePHrr99ttVUlJy3rQDBw6oX79+SkxMlCTt3LlT77//voKDg5Weni5/f38NHjxYw4YN0/fff6///u//1rp16yRJjz76KAcI1ElYWJi++OIL2Wy2i45zcrvdqqysVGhoqMaNG6cePXooMTFRrVq18mxnDodDQ4cOlb+/v6qqqnTddddp9uzZkqSJEyfqgw8+qPY5brdbLperWu/fz9t37txZ7aCCpq2goEDDhg2TJPXu3VtDhgxRQECAKioq5O/vL6vVKqfTqSFDhqisrEyTJk3SihUrFBkZKX//ix9Kg4KCVFlZKT8/P8b9XQKePdRIbN26VRMmTFBgYKBnJ+90OmWz2eRyuVRVVeUJDi6XS+Xl5crMzFRhYaGmT5+u7du3a9GiRVq4cKHCwsJUWVmpEydOqEWLFpLODp7Mzs7WtddeqxdeeOGCB4hfBhkOENi6dauefPJJxcTE1Dj95MmTeuaZZ/TYY49Va+/cubM2btyoK6+8UtLZMS3Z2dk1jmlxuVzVTkFJ0qeffqoXX3xR27ZtO+8gce67AfzcmjVrdNddd1UbOP7//t//U69evTRgwIBq87rdbs2bN0+vvfbaedteVVXVBfeFa9eu1bXXXnv5folGjp6WRqJ79+7atGlTnd/XqVMnvfzyy5KkgIAADR06tFpPy4YNGySd7Wk5Z9q0aZoxY0a15XCAwIXYbDa1atVKo0aNqnH6smXLPIG2oqJCbrfb85ftr3G5XKqoqJDNZvvVeWfNmqWDBw9q3Lhxat26tacm4JceeOABVVRU1Bg6zqmsrJTb7VZAQIBGjhx53nb97bff6v7779f27ds9V8Odc66XGvVHaGlk3n//fU2ePFnR0dE1Tj927Jhee+019e7d+7xpVVVVWrRokd577z1PT0ufPn0kne1pOYcDBOri3Omc06dP1zj951dVLF++XO+88478/Px01VVX6Xe/+51nPofDoWHDhlXrMayqqtKSJUs8PYI1GTVqlBYvXqy0tDQNHDhQTz/9NL1+uKCpU6dq7dq1ntBSVlamTZs2adq0aZ7ekv/6r//SuHHjfnVfuHLlSm3atEm///3vPQN3OU1+6QgtjYzNZlO3bt2UlZVV4/SHH374giGiqqrqgj0tAwYMqNV5WA4QqMnhw4c1b968GqedPHnS89+DBw/W4MGDa5wvOTlZCxcurPUlz+fYbDY9+eSTuvXWWzVlyhTt379fnTp1qtMy0HT88Y9/1DXXXKPU1FTFx8dXOz20adMmFRYWXnAb/bmHH35YoaGhGjJkiPr27avnnnvOc6oT9UdoaWQCAgL0+eefKzk5ucbpp0+fPu+vg7KyMp04ceKCA2YfeeQR5eXlKSoq6qKfzwECv+RyudSpUyctXbr0vPbjx49r/PjxtQrEVVVVqqqqqvXnVlVVqbKy0vP62muv1bJly2pfOJqsY8eO6YUXXjhvm120aJH69etX6+U8/PDD6tWrlyZMmKCdO3f+6tV0qB1CSyNTUVGhnj17/mpPi8vlknR2rMm0adMUGRmpAQMGqF27duratasOHDggq9WqYcOG6ciRIxo4cKAeffRR5ebmqkuXLrriiivOWy4HCFzIhR4NYbFYNHr0aDkcjl+9lP6c8vJyVVRU1Ppzy8vLdfr0abndbq7WQK1UVVXJYrFo1KhReuCBB5SXl+eZtmnTJjmdTg0ePFhVVVWesVc1LePnP6+66iruz9KACC2NjNPp1LZt2zxjUX7p2LFjnoPIBx98oO+++06ffPKJzpw5o48//lhbt27VRx99pBMnTqi8vFzTp0/3PLslOjrac7rolzhA4EK6deumbt26nddusVj07rvv1npg4r59++r0uf3791f//v3r9B40befuz3JucPcjjzwil8ulLVu2ePaDycnJqqysVEpKSo33Gjp3byq73a6IiAiv1t8UcMlzE1daWqqwsDCjywAA4KIILQAAwBS4YBwAAJgCoQUAAJgCoQUAAJgCoQUAAJgCoQUAAJgCoQUAAJgCoQWAYR599FHl5OQYXQYAkyC0AAAAU+DmcgC8btKkSVqxYkW1toEDB6pLly5atGiRPvzwQ0mSw+HQzTffrDVr1uibb77R8uXLFRMTo+3bt6tTp06aPn26mjdvLknavXu3pk6dqoMHD+rmm2/W9OnTudsz0MjQ0wLA68aPH68dO3aoS5cumjRpknbs2KHx48frtttuU0FBgQ4ePChJ2rJli66++mq1bdtWkvR///d/6ty5s95//33ZbDZNnjxZklRSUqKhQ4fqlltu0V/+8hfZ7XbNmDHDsN8PwOVBaAHgdUFBQQoPD5e/v7+Cg4MVHh6uoKAghYaGqlevXvrkk08kSZ9++qnuuusuz/tatmypoUOHKj4+XqNGjdLmzZtVWVmpv/3tbwoICNDTTz+t2NhYPf7449q0aZNRvx6Ay4SnPAPwKXfeeacWL16sIUOGaPPmzXrmmWc801q2bOl5iniLFi1UVVWl4uJiHTlyREVFRUpJSZEkuVwuORwOlZeXKzAw0JDfA0DDI7QAMIzFYtEvh9X16dNHEyZMUE5Ojlq1aqVWrVp5pv3www9yu92yWCz68ccf5e/vr6ioKLVs2VKJiYl69dVXJUlut1t2u13+/uzigMaE00MADNOqVStt27ZNx44d09atW1VVVaWQkBDdcssteumll6qdGpKkY8eOKSsrS4WFhZo3b5769u0rPz8/paam6scff9Tu3bsVFBSkjz/+WE888cR5gQiAuRFaABhmxIgRKiwsVJ8+fTR58mS5XC5JZ08RlZaW6s4776w2f6dOnbR7927de++9qqio0MSJEyVJ4eHhmj9/vhYvXqzbbrtN//u//6sFCxbQ0wI0MnyjARgmNjZW7733XrW2wsJClZeXq3PnzoqNja02zWazaf78+TUuq2PHjlq1atVlqxWA8QgtAHzKiBEjVFRUpNmzZxtdCgAfw83lAACAKTCmBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmAKhBQAAmML/B4cPHcCYPx+cAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4、不同类型卡的持卡人在办卡时的平均年龄对比\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "data['age'] = (pd.to_datetime(data['issued']) - pd.to_datetime(data['birth_date']))\n",
    "\n",
    "data['age1'] = data['age'].map(lambda x: x.days / 365)\n",
    "sns.boxplot(x='type', y='age1', data=data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:35.501199Z",
     "end_time": "2024-06-19T10:57:35.733369Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_19740\\2384847290.py:2: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  trans = pd.read_csv(r\"../data/bank/trans.csv\", encoding=\"gbk\")\n"
     ]
    },
    {
     "data": {
      "text/plain": "        card_id  disp_id      issued type_x  client_id  account_id type_y  \\\n0          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n1          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n2          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n3          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n4          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n...         ...      ...         ...    ...        ...         ...    ...   \n221933      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221934      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221935      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221936      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221937      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n\n        trans_id        date type operation  amount balance k_symbol bank  \\\n0        2349697  1993-02-08    贷      信贷资金    $600    $600      NaN  NaN   \n1        2349709  1993-02-12    贷      信贷资金  $19588  $20188      NaN  NaN   \n2        2349705  1993-02-12    贷      信贷资金  $27078  $47266      NaN  NaN   \n3        3492040  1993-02-28    贷       NaN    $120  $47386     利息所得  NaN   \n4        2350078  1993-03-10    借        现金  $12000  $35386      NaN  NaN   \n...          ...         ...  ...       ...     ...     ...      ...  ...   \n221933    990688  1998-11-30    借        现金     $15  $38361       支票  NaN   \n221934    990624  1998-12-05    借        现金  $1,760  $36601      NaN  NaN   \n221935    990615  1998-12-09    借  汇款到另一家银行  $3,240  $33361     房屋贷款   QR   \n221936    990579  1998-12-14    贷      信贷资金  $31752  $65113      NaN  NaN   \n221937   3642207  1998-12-31    贷       NaN    $220  $65333     利息所得  NaN   \n\n           account  \n0              NaN  \n1              NaN  \n2              NaN  \n3              NaN  \n4              NaN  \n...            ...  \n221933         NaN  \n221934         NaN  \n221935  72621738.0  \n221936         NaN  \n221937         NaN  \n\n[221938 rows x 16 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>card_id</th>\n      <th>disp_id</th>\n      <th>issued</th>\n      <th>type_x</th>\n      <th>client_id</th>\n      <th>account_id</th>\n      <th>type_y</th>\n      <th>trans_id</th>\n      <th>date</th>\n      <th>type</th>\n      <th>operation</th>\n      <th>amount</th>\n      <th>balance</th>\n      <th>k_symbol</th>\n      <th>bank</th>\n      <th>account</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349697</td>\n      <td>1993-02-08</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$600</td>\n      <td>$600</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349709</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$19588</td>\n      <td>$20188</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349705</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$27078</td>\n      <td>$47266</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>3492040</td>\n      <td>1993-02-28</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$120</td>\n      <td>$47386</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2350078</td>\n      <td>1993-03-10</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$12000</td>\n      <td>$35386</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>221933</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990688</td>\n      <td>1998-11-30</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$15</td>\n      <td>$38361</td>\n      <td>支票</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>221934</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990624</td>\n      <td>1998-12-05</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$1,760</td>\n      <td>$36601</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>221935</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990615</td>\n      <td>1998-12-09</td>\n      <td>借</td>\n      <td>汇款到另一家银行</td>\n      <td>$3,240</td>\n      <td>$33361</td>\n      <td>房屋贷款</td>\n      <td>QR</td>\n      <td>72621738.0</td>\n    </tr>\n    <tr>\n      <th>221936</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990579</td>\n      <td>1998-12-14</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$31752</td>\n      <td>$65113</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>221937</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>3642207</td>\n      <td>1998-12-31</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$220</td>\n      <td>$65333</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>221938 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 、不同类型卡的持卡人在办卡前一年内的平均帐户余额对比\n",
    "trans = pd.read_csv(r\"../data/bank/trans.csv\", encoding=\"gbk\")\n",
    "data2 = pd.merge(card, disp, on=\"disp_id\", how=\"left\")\n",
    "data2 = pd.merge(data2, trans, on=\"account_id\", how=\"left\")\n",
    "data2"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:35.667348Z",
     "end_time": "2024-06-19T10:57:37.505469Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "        card_id  disp_id      issued type_x  client_id  account_id type_y  \\\n0          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n1          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n2          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n3          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n4          1005     9285  1993-11-07    普通卡       9593        7753    所有者   \n...         ...      ...         ...    ...        ...         ...    ...   \n221933      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221934      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221935      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221936      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n221937      635     4083  1998-12-29    普通卡       4083        3377    所有者   \n\n        trans_id        date type operation  amount balance k_symbol bank  \\\n0        2349697  1993-02-08    贷      信贷资金    $600    $600      NaN  NaN   \n1        2349709  1993-02-12    贷      信贷资金  $19588  $20188      NaN  NaN   \n2        2349705  1993-02-12    贷      信贷资金  $27078  $47266      NaN  NaN   \n3        3492040  1993-02-28    贷       NaN    $120  $47386     利息所得  NaN   \n4        2350078  1993-03-10    借        现金  $12000  $35386      NaN  NaN   \n...          ...         ...  ...       ...     ...     ...      ...  ...   \n221933    990688  1998-11-30    借        现金     $15  $38361       支票  NaN   \n221934    990624  1998-12-05    借        现金  $1,760  $36601      NaN  NaN   \n221935    990615  1998-12-09    借  汇款到另一家银行  $3,240  $33361     房屋贷款   QR   \n221936    990579  1998-12-14    贷      信贷资金  $31752  $65113      NaN  NaN   \n221937   3642207  1998-12-31    贷       NaN    $220  $65333     利息所得  NaN   \n\n           account  \n0              NaN  \n1              NaN  \n2              NaN  \n3              NaN  \n4              NaN  \n...            ...  \n221933         NaN  \n221934         NaN  \n221935  72621738.0  \n221936         NaN  \n221937         NaN  \n\n[221938 rows x 16 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>card_id</th>\n      <th>disp_id</th>\n      <th>issued</th>\n      <th>type_x</th>\n      <th>client_id</th>\n      <th>account_id</th>\n      <th>type_y</th>\n      <th>trans_id</th>\n      <th>date</th>\n      <th>type</th>\n      <th>operation</th>\n      <th>amount</th>\n      <th>balance</th>\n      <th>k_symbol</th>\n      <th>bank</th>\n      <th>account</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349697</td>\n      <td>1993-02-08</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$600</td>\n      <td>$600</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349709</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$19588</td>\n      <td>$20188</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349705</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$27078</td>\n      <td>$47266</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>3492040</td>\n      <td>1993-02-28</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$120</td>\n      <td>$47386</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2350078</td>\n      <td>1993-03-10</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$12000</td>\n      <td>$35386</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>221933</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990688</td>\n      <td>1998-11-30</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$15</td>\n      <td>$38361</td>\n      <td>支票</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>221934</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990624</td>\n      <td>1998-12-05</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$1,760</td>\n      <td>$36601</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>221935</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990615</td>\n      <td>1998-12-09</td>\n      <td>借</td>\n      <td>汇款到另一家银行</td>\n      <td>$3,240</td>\n      <td>$33361</td>\n      <td>房屋贷款</td>\n      <td>QR</td>\n      <td>72621738.0</td>\n    </tr>\n    <tr>\n      <th>221936</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990579</td>\n      <td>1998-12-14</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$31752</td>\n      <td>$65113</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>221937</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>3642207</td>\n      <td>1998-12-31</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$220</td>\n      <td>$65333</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>221938 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data2[data2['type_y'] == '所有者']\n",
    "data2"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:37.503463Z",
     "end_time": "2024-06-19T10:57:37.668934Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "        card_id  disp_id     issued type_x  client_id  account_id type_y  \\\n0          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n1          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n2          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n3          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n4          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n...         ...      ...        ...    ...        ...         ...    ...   \n221933      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221934      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221935      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221936      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221937      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n\n        trans_id        date type operation  amount balance k_symbol bank  \\\n0        2349697  1993-02-08    贷      信贷资金    $600    $600      NaN  NaN   \n1        2349709  1993-02-12    贷      信贷资金  $19588  $20188      NaN  NaN   \n2        2349705  1993-02-12    贷      信贷资金  $27078  $47266      NaN  NaN   \n3        3492040  1993-02-28    贷       NaN    $120  $47386     利息所得  NaN   \n4        2350078  1993-03-10    借        现金  $12000  $35386      NaN  NaN   \n...          ...         ...  ...       ...     ...     ...      ...  ...   \n221933    990688  1998-11-30    借        现金     $15  $38361       支票  NaN   \n221934    990624  1998-12-05    借        现金  $1,760  $36601      NaN  NaN   \n221935    990615  1998-12-09    借  汇款到另一家银行  $3,240  $33361     房屋贷款   QR   \n221936    990579  1998-12-14    贷      信贷资金  $31752  $65113      NaN  NaN   \n221937   3642207  1998-12-31    贷       NaN    $220  $65333     利息所得  NaN   \n\n           account     t_date  balance2  amount2  \n0              NaN 1993-02-08       600      600  \n1              NaN 1993-02-12     20188    19588  \n2              NaN 1993-02-12     47266    27078  \n3              NaN 1993-02-28     47386      120  \n4              NaN 1993-03-10     35386    12000  \n...            ...        ...       ...      ...  \n221933         NaN 1998-11-30     38361       15  \n221934         NaN 1998-12-05     36601     1760  \n221935  72621738.0 1998-12-09     33361     3240  \n221936         NaN 1998-12-14     65113    31752  \n221937         NaN 1998-12-31     65333      220  \n\n[221938 rows x 19 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>card_id</th>\n      <th>disp_id</th>\n      <th>issued</th>\n      <th>type_x</th>\n      <th>client_id</th>\n      <th>account_id</th>\n      <th>type_y</th>\n      <th>trans_id</th>\n      <th>date</th>\n      <th>type</th>\n      <th>operation</th>\n      <th>amount</th>\n      <th>balance</th>\n      <th>k_symbol</th>\n      <th>bank</th>\n      <th>account</th>\n      <th>t_date</th>\n      <th>balance2</th>\n      <th>amount2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349697</td>\n      <td>1993-02-08</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$600</td>\n      <td>$600</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-08</td>\n      <td>600</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349709</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$19588</td>\n      <td>$20188</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-12</td>\n      <td>20188</td>\n      <td>19588</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349705</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$27078</td>\n      <td>$47266</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-12</td>\n      <td>47266</td>\n      <td>27078</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>3492040</td>\n      <td>1993-02-28</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$120</td>\n      <td>$47386</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-28</td>\n      <td>47386</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2350078</td>\n      <td>1993-03-10</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$12000</td>\n      <td>$35386</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-03-10</td>\n      <td>35386</td>\n      <td>12000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>221933</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990688</td>\n      <td>1998-11-30</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$15</td>\n      <td>$38361</td>\n      <td>支票</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-11-30</td>\n      <td>38361</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>221934</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990624</td>\n      <td>1998-12-05</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$1,760</td>\n      <td>$36601</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-12-05</td>\n      <td>36601</td>\n      <td>1760</td>\n    </tr>\n    <tr>\n      <th>221935</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990615</td>\n      <td>1998-12-09</td>\n      <td>借</td>\n      <td>汇款到另一家银行</td>\n      <td>$3,240</td>\n      <td>$33361</td>\n      <td>房屋贷款</td>\n      <td>QR</td>\n      <td>72621738.0</td>\n      <td>1998-12-09</td>\n      <td>33361</td>\n      <td>3240</td>\n    </tr>\n    <tr>\n      <th>221936</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990579</td>\n      <td>1998-12-14</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$31752</td>\n      <td>$65113</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-12-14</td>\n      <td>65113</td>\n      <td>31752</td>\n    </tr>\n    <tr>\n      <th>221937</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>3642207</td>\n      <td>1998-12-31</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$220</td>\n      <td>$65333</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-12-31</td>\n      <td>65333</td>\n      <td>220</td>\n    </tr>\n  </tbody>\n</table>\n<p>221938 rows × 19 columns</p>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['issued'] = pd.to_datetime(data2['issued'])\n",
    "data2['t_date'] = pd.to_datetime(data2['date'])\n",
    "data2['balance2'] = data2['balance'].map(\n",
    "    lambda x: int(''.join(x[1:].split(','))))\n",
    "data2['amount2'] = data2['amount'].map(\n",
    "    lambda x: int(''.join(x[1:].split(','))))\n",
    "data2"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:37.611924Z",
     "end_time": "2024-06-19T10:57:38.048121Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_19740\\1686597555.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data3 = data2[data2.issued > data2.t_date][\n"
     ]
    },
    {
     "data": {
      "text/plain": "        card_id  disp_id     issued type_x  client_id  account_id type_y  \\\n0          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n1          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n2          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n3          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n4          1005     9285 1993-11-07    普通卡       9593        7753    所有者   \n...         ...      ...        ...    ...        ...         ...    ...   \n221932      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221933      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221934      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221935      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n221936      635     4083 1998-12-29    普通卡       4083        3377    所有者   \n\n        trans_id        date type operation  amount balance k_symbol bank  \\\n0        2349697  1993-02-08    贷      信贷资金    $600    $600      NaN  NaN   \n1        2349709  1993-02-12    贷      信贷资金  $19588  $20188      NaN  NaN   \n2        2349705  1993-02-12    贷      信贷资金  $27078  $47266      NaN  NaN   \n3        3492040  1993-02-28    贷       NaN    $120  $47386     利息所得  NaN   \n4        2350078  1993-03-10    借        现金  $12000  $35386      NaN  NaN   \n...          ...         ...  ...       ...     ...     ...      ...  ...   \n221932   3642206  1998-11-30    贷       NaN    $184  $38376     利息所得  NaN   \n221933    990688  1998-11-30    借        现金     $15  $38361       支票  NaN   \n221934    990624  1998-12-05    借        现金  $1,760  $36601      NaN  NaN   \n221935    990615  1998-12-09    借  汇款到另一家银行  $3,240  $33361     房屋贷款   QR   \n221936    990579  1998-12-14    贷      信贷资金  $31752  $65113      NaN  NaN   \n\n           account     t_date  balance2  amount2  \n0              NaN 1993-02-08       600      600  \n1              NaN 1993-02-12     20188    19588  \n2              NaN 1993-02-12     47266    27078  \n3              NaN 1993-02-28     47386      120  \n4              NaN 1993-03-10     35386    12000  \n...            ...        ...       ...      ...  \n221932         NaN 1998-11-30     38376      184  \n221933         NaN 1998-11-30     38361       15  \n221934         NaN 1998-12-05     36601     1760  \n221935  72621738.0 1998-12-09     33361     3240  \n221936         NaN 1998-12-14     65113    31752  \n\n[63118 rows x 19 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>card_id</th>\n      <th>disp_id</th>\n      <th>issued</th>\n      <th>type_x</th>\n      <th>client_id</th>\n      <th>account_id</th>\n      <th>type_y</th>\n      <th>trans_id</th>\n      <th>date</th>\n      <th>type</th>\n      <th>operation</th>\n      <th>amount</th>\n      <th>balance</th>\n      <th>k_symbol</th>\n      <th>bank</th>\n      <th>account</th>\n      <th>t_date</th>\n      <th>balance2</th>\n      <th>amount2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349697</td>\n      <td>1993-02-08</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$600</td>\n      <td>$600</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-08</td>\n      <td>600</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349709</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$19588</td>\n      <td>$20188</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-12</td>\n      <td>20188</td>\n      <td>19588</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2349705</td>\n      <td>1993-02-12</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$27078</td>\n      <td>$47266</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-12</td>\n      <td>47266</td>\n      <td>27078</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>3492040</td>\n      <td>1993-02-28</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$120</td>\n      <td>$47386</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-02-28</td>\n      <td>47386</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1005</td>\n      <td>9285</td>\n      <td>1993-11-07</td>\n      <td>普通卡</td>\n      <td>9593</td>\n      <td>7753</td>\n      <td>所有者</td>\n      <td>2350078</td>\n      <td>1993-03-10</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$12000</td>\n      <td>$35386</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1993-03-10</td>\n      <td>35386</td>\n      <td>12000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>221932</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>3642206</td>\n      <td>1998-11-30</td>\n      <td>贷</td>\n      <td>NaN</td>\n      <td>$184</td>\n      <td>$38376</td>\n      <td>利息所得</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-11-30</td>\n      <td>38376</td>\n      <td>184</td>\n    </tr>\n    <tr>\n      <th>221933</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990688</td>\n      <td>1998-11-30</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$15</td>\n      <td>$38361</td>\n      <td>支票</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-11-30</td>\n      <td>38361</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>221934</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990624</td>\n      <td>1998-12-05</td>\n      <td>借</td>\n      <td>现金</td>\n      <td>$1,760</td>\n      <td>$36601</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-12-05</td>\n      <td>36601</td>\n      <td>1760</td>\n    </tr>\n    <tr>\n      <th>221935</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990615</td>\n      <td>1998-12-09</td>\n      <td>借</td>\n      <td>汇款到另一家银行</td>\n      <td>$3,240</td>\n      <td>$33361</td>\n      <td>房屋贷款</td>\n      <td>QR</td>\n      <td>72621738.0</td>\n      <td>1998-12-09</td>\n      <td>33361</td>\n      <td>3240</td>\n    </tr>\n    <tr>\n      <th>221936</th>\n      <td>635</td>\n      <td>4083</td>\n      <td>1998-12-29</td>\n      <td>普通卡</td>\n      <td>4083</td>\n      <td>3377</td>\n      <td>所有者</td>\n      <td>990579</td>\n      <td>1998-12-14</td>\n      <td>贷</td>\n      <td>信贷资金</td>\n      <td>$31752</td>\n      <td>$65113</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1998-12-14</td>\n      <td>65113</td>\n      <td>31752</td>\n    </tr>\n  </tbody>\n</table>\n<p>63118 rows × 19 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "data3 = data2[data2.issued > data2.t_date][\n",
    "    data2.issued < data2.t_date + datetime.timedelta(days=365)]\n",
    "data3"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:38.046103Z",
     "end_time": "2024-06-19T10:57:38.214104Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='type_x', ylabel='avg_balance'>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data4 = data3.groupby(['type_x', 'card_id'])['balance2'].agg([('avg_balance', 'mean')])\n",
    "data5 = data4.reset_index()\n",
    "sns.boxplot(x='type_x', y='avg_balance', data=data5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:38.118107Z",
     "end_time": "2024-06-19T10:57:38.382451Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "                       amount2\ntype_x card_id type1          \n普通卡    2       income   193911\n               out      196384\n       4       income   474142\n               out      357224\n       7       income   299797",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>amount2</th>\n    </tr>\n    <tr>\n      <th>type_x</th>\n      <th>card_id</th>\n      <th>type1</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">普通卡</th>\n      <th rowspan=\"2\" valign=\"top\">2</th>\n      <th>income</th>\n      <td>193911</td>\n    </tr>\n    <tr>\n      <th>out</th>\n      <td>196384</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">4</th>\n      <th>income</th>\n      <td>474142</td>\n    </tr>\n    <tr>\n      <th>out</th>\n      <td>357224</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <th>income</th>\n      <td>299797</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6、不同类型卡的持卡人在办卡前一年内的平均收入对比\n",
    "type_dict = {'借': 'out', '贷': 'income'}\n",
    "data3['type1'] = data3['type'].map(type_dict)\n",
    "dat6 = data3.groupby(['type_x', 'card_id', 'type1'])[['amount2']].sum()\n",
    "dat6.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:38.296530Z",
     "end_time": "2024-06-19T10:57:38.397444Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='type_x', ylabel='amount2'>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data7 = dat6.reset_index()\n",
    "data8 = data7[data7['type1'] == 'income']\n",
    "sns.boxplot(x='type_x', y='amount2', data=data8)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:38.320446Z",
     "end_time": "2024-06-19T10:57:38.543854Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: xlabel='type_x', ylabel='amount2'>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data9 = data7[data7['type1'] == 'out']\n",
    "sns.boxplot(x='type_x', y='amount2', data=data9)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-06-19T10:57:38.474853Z",
     "end_time": "2024-06-19T10:57:38.786688Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [],
   "metadata": {
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